Authors : Aniket Gursale

Volume/Issue : Volume 9 - 2024, Issue 11 - November

Google Scholar : https://tinyurl.com/3s2wehx3

Scribd : https://tinyurl.com/mvka4pkk

DOI : https://doi.org/10.38124/ijisrt/IJISRT24NOV671

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Dynamic pricing serves as an essential tactic in the airline sector, allowing airlines to modify ticket rates in response to changing market demand, rivalry, and various other influencing elements. This research investigates the use of Reinforcement Learning (RL) in dynamic pricing strategies, emphasizing its ability to boost revenue management and increase customer satisfaction. In contrast to conventional pricing strategies, RL allows airlines to adjust prices in real-time by continuously analyzing environmental data such as seat availability, departure time, and competitor pricing. This study explores current pricing models, the framework of RL-driven dynamic pricing, and a case analysis to showcase the real-world advantages and difficulties of RL. Core discoveries reveal that RL-driven dynamic pricing provides considerable benefits in responding to real-time demand fluctuations, thereby optimizing revenue opportunities. Nonetheless, obstacles like limited data, high computational demands, and striking a balance between exploration and exploitation still persist. The research ends with observations on how RL can further reshape airline revenue management and suggests future research avenues to improve its practical uses.

Keywords : Dynamic Pricing, Reinforcement Learning, Airline Revenue Management, Machine Learning, Optimization, Predictive Models, Customer Demand.

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